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import copy |
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import tempfile |
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import torch |
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import numpy as np |
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import torchio as tio |
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from ..utils import TorchioTestCase |
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class TestSubject(TorchioTestCase): |
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"""Tests for `Subject`.""" |
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def test_positional_args(self): |
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with self.assertRaises(ValueError): |
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tio.Subject(0) |
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def test_input_dict(self): |
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with tempfile.NamedTemporaryFile(delete=False) as f: |
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input_dict = {'image': tio.ScalarImage(f.name)} |
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tio.Subject(input_dict) |
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tio.Subject(**input_dict) |
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def test_no_sample(self): |
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with tempfile.NamedTemporaryFile(delete=False) as f: |
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input_dict = {'image': tio.ScalarImage(f.name)} |
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subject = tio.Subject(input_dict) |
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with self.assertRaises(RuntimeError): |
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with self.assertWarns(UserWarning): |
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tio.RandomFlip()(subject) |
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def test_history(self): |
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transformed = tio.RandomGamma()(self.sample_subject) |
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self.assertIs(len(transformed.history), 1) |
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def test_inconsistent_shape(self): |
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subject = tio.Subject( |
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a=tio.ScalarImage(tensor=torch.rand(1, 2, 3, 4)), |
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b=tio.ScalarImage(tensor=torch.rand(2, 2, 3, 4)), |
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) |
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subject.spatial_shape |
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with self.assertRaises(RuntimeError): |
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subject.shape |
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def test_inconsistent_spatial_shape(self): |
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subject = tio.Subject( |
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a=tio.ScalarImage(tensor=torch.rand(1, 3, 3, 4)), |
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b=tio.ScalarImage(tensor=torch.rand(2, 2, 3, 4)), |
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) |
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with self.assertRaises(RuntimeError): |
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subject.spatial_shape |
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def test_plot(self): |
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self.sample_subject.plot( |
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show=False, |
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output_path=self.dir / 'figure.png', |
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cmap_dict=dict( |
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t2='viridis', |
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label={0: 'yellow', 1: 'blue'}, |
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), |
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) |
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60
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def test_plot_one_image(self): |
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subject = tio.Subject(t1=tio.ScalarImage(self.get_image_path('t1_plot'))) |
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subject.plot(show=False) |
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64
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# flake8: noqa: E203, E241 |
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def test_same_space(self): |
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# https://github.com/fepegar/torchio/issues/381 |
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affine1 = np.array([ |
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[ 4.27109375e-14, -8.71264808e-03, 9.99876633e-01, -3.39850907e+01], |
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[-5.54687500e-01, -2.71630469e-12, 8.75148028e-17, 1.62282930e+02], |
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[ 2.71575000e-12, -5.54619070e-01, -1.57073092e-02, 2.28515784e+02], |
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[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00], |
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]) |
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affine2 = np.array([ |
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[ 3.67499773e-08, -8.71257665e-03, 9.99876635e-01, -3.39850922e+01], |
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[-5.54687500e-01, 3.67499771e-08, 6.73024385e-08, 1.62282928e+02], |
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[-3.73318194e-08, -5.54619071e-01, -1.57071802e-02, 2.28515778e+02], |
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[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00], |
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]) |
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t = torch.rand(1, 2, 3, 4) |
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subject = tio.Subject( |
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im1=tio.ScalarImage(tensor=t, affine=affine1), |
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im2=tio.ScalarImage(tensor=t, affine=affine2), |
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) |
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subject.check_consistent_space() |
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def test_delete_image(self): |
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subject = copy.deepcopy(self.sample_subject) |
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subject.remove_image('t1') |
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with self.assertRaises(KeyError): |
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subject['t1'] |
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with self.assertRaises(AttributeError): |
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subject.t1 |
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def test_2d(self): |
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subject = self.make_2d(self.sample_subject) |
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assert subject.is_2d() |
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def test_different_non_numeric(self): |
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with self.assertRaises(RuntimeError): |
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self.sample_subject.check_consistent_attribute('path') |
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def test_bad_arg(self): |
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with self.assertRaises(ValueError): |
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tio.Subject(0) |
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def test_no_images(self): |
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with self.assertRaises(ValueError): |
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tio.Subject(a=0) |
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fepegar /
torchio
